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  • Azure Grid Computing - Worker Roles as HPC Compute Nodes

    - by JoshReuben
    Overview ·        With HPC 2008 R2 SP1 You can add Azure worker roles as compute nodes in a local Windows HPC Server cluster. ·        The subscription for Windows Azure like any other Azure Service - charged for the time that the role instances are available, as well as for the compute and storage services that are used on the nodes. ·        Win-Win ? - Azure charges the computer hour cost (according to vm size) amortized over a month – so you save on purchasing compute node hardware. Microsoft wins because you need to purchase HPC to have a local head node for managing this compute cluster grid distributed in the cloud. ·        Blob storage is used to hold input & output files of each job. I can see how Parametric Sweep HPC jobs can be supported (where the same job is run multiple times on each node against different input units), but not MPI.NET (where different HPC Job instances function as coordinated agents and conduct master-slave inter-process communication), unless Azure is somehow tunneling MPI communication through inter-WorkerRole Azure Queues. ·        this is not the end of the story for Azure Grid Computing. If MS requires you to purchase a local HPC license (and administrate it), what's to stop a 3rd party from doing this and encapsulating exposing HPC WCF Broker Service to you for managing compute nodes? If MS doesn’t  provide head node as a service, someone else will! Process ·        requires creation of a worker node template that specifies a connection to an existing subscription for Windows Azure + an availability policy for the worker nodes. ·        After worker nodes are added to the cluster, you can start them, which provisions the Windows Azure role instances, and then bring them online to run HPC cluster jobs. ·        A Windows Azure worker role instance runs a HPC compatible Azure guest operating system which runs on the VMs that host your service. The guest operating system is updated monthly. You can choose to upgrade the guest OS for your service automatically each time an update is released - All role instances defined by your service will run on the guest operating system version that you specify. see Windows Azure Guest OS Releases and SDK Compatibility Matrix (http://go.microsoft.com/fwlink/?LinkId=190549). ·        use the hpcpack command to upload file packages and install files to run on the worker nodes. see hpcpack (http://go.microsoft.com/fwlink/?LinkID=205514). Requirements ·        assuming you have an azure subscription account and the HPC head node installed and configured. ·        Install HPC Pack 2008 R2 SP 1 -  see Microsoft HPC Pack 2008 R2 Service Pack 1 Release Notes (http://go.microsoft.com/fwlink/?LinkID=202812). ·        Configure the head node to connect to the Internet - connectivity is provided by the connection of the head node to the enterprise network. You may need to configure a proxy client on the head node. Any cluster network topology (1-5) is supported). ·        Configure the firewall - allow outbound TCP traffic on the following ports: 80,       443, 5901, 5902, 7998, 7999 ·        Note: HPC Server  uses Admin Mode (Elevated Privileges) in Windows Azure to give the service administrator of the subscription the necessary privileges to initialize HPC cluster services on the worker nodes. ·        Obtain a Windows Azure subscription certificate - the Windows Azure subscription must be configured with a public subscription (API) certificate -a valid X.509 certificate with a key size of at least 2048 bits. Generate a self-sign certificate & upload a .cer file to the Windows Azure Portal Account page > Manage my API Certificates link. see Using the Windows Azure Service Management API (http://go.microsoft.com/fwlink/?LinkId=205526). ·        import the certificate with an associated private key on the HPC cluster head node - into the trusted root store of the local computer account. Obtain Windows Azure Connection Information for HPC Server ·        required for each worker node template ·        copy from azure portal - Get from: navigation pane > Hosted Services > Storage Accounts & CDN ·        Subscription ID - a 32-char hex string in the form xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx. In Properties pane. ·        Subscription certificate thumbprint - a 40-char hex string (you need to remove spaces). In Management Certificates > Properties pane. ·        Service name - the value of <ServiceName> configured in the public URL of the service (http://<ServiceName>.cloudapp.net). In Hosted Services > Properties pane. ·        Blob Storage account name - the value of <StorageAccountName> configured in the public URL of the account (http://<StorageAccountName>.blob.core.windows.net). In Storage Accounts > Properties pane. Import the Azure Subscription Certificate on the HPC Head Node ·        enable the services for Windows HPC Server  to authenticate properly with the Windows Azure subscription. ·        use the Certificates MMC snap-in to import the certificate to the Trusted Root Certification Authorities store of the local computer account. The certificate must be in PFX format (.pfx or .p12 file) with a private key that is protected by a password. ·        see Certificates (http://go.microsoft.com/fwlink/?LinkId=163918). ·        To open the certificates snapin: Run > mmc. File > Add/Remove Snap-in > certificates > Computer account > Local Computer ·        To import the certificate via wizard - Certificates > Trusted Root Certification Authorities > Certificates > All Tasks > Import ·        After the certificate is imported, it appears in the details pane in the Certificates snap-in. You can open the certificate to check its status. Configure a Proxy Client on the HPC Head Node ·        the following Windows HPC Server services must be able to communicate over the Internet (through the firewall) with the services for Windows Azure: HPCManagement, HPCScheduler, HPCBrokerWorker. ·        Create a Windows Azure Worker Node Template ·        Edit HPC node templates in HPC Node Template Editor. ·        Specify: 1) Windows Azure subscription connection info (unique service name) for adding a set of worker nodes to the cluster + 2)worker node availability policy – rules for deploying / removing worker role instances in Windows Azure o   HPC Cluster Manager > Configuration > Navigation Pane > Node Templates > Actions pane > New à Create Node Template Wizard or Edit à Node Template Editor o   Choose Node Template Type page - Windows Azure worker node template o   Specify Template Name page – template name & description o   Provide Connection Information page – Azure Subscription ID (text) & Subscription certificate (browse) o   Provide Service Information page - Azure service name + blob storage account name (optionally click Retrieve Connection Information to get list of available from azure – possible LRT). o   Configure Azure Availability Policy page - how Windows Azure worker nodes start / stop (online / offline the worker role instance -  add / remove) – manual / automatic o   for automatic - In the Configure Windows Azure Worker Availability Policy dialog -select days and hours for worker nodes to start / stop. ·        To validate the Windows Azure connection information, on the template's Connection Information tab > Validate connection information. ·        You can upload a file package to the storage account that is specified in the template - eg upload application or service files that will run on the worker nodes. see hpcpack (http://go.microsoft.com/fwlink/?LinkID=205514). Add Azure Worker Nodes to the HPC Cluster ·        Use the Add Node Wizard – specify: 1) the worker node template, 2) The number of worker nodes   (within the quota of role instances in the azure subscription), and 3)           The VM size of the worker nodes : ExtraSmall, Small, Medium, Large, or ExtraLarge.  ·        to add worker nodes of different sizes, must run the Add Node Wizard separately for each size. ·        All worker nodes that are added to the cluster by using a specific worker node template define a set of worker nodes that will be deployed and managed together in Windows Azure when you start the nodes. This includes worker nodes that you add later by using the worker node template and, if you choose, worker nodes of different sizes. You cannot start, stop, or delete individual worker nodes. ·        To add Windows Azure worker nodes o   In HPC Cluster Manager: Node Management > Actions pane > Add Node à Add Node Wizard o   Select Deployment Method page - Add Azure Worker nodes o   Specify New Nodes page - select a worker node template, specify the number and size of the worker nodes ·        After you add worker nodes to the cluster, they are in the Not-Deployed state, and they have a health state of Unapproved. Before you can use the worker nodes to run jobs, you must start them and then bring them online. ·        Worker nodes are numbered consecutively in a naming series that begins with the root name AzureCN – this is non-configurable. Deploying Windows Azure Worker Nodes ·        To deploy the role instances in Windows Azure - start the worker nodes added to the HPC cluster and bring the nodes online so that they are available to run cluster jobs. This can be configured in the HPC Azure Worker Node Template – Azure Availability Policy -  to be automatic or manual. ·        The Start, Stop, and Delete actions take place on the set of worker nodes that are configured by a specific worker node template. You cannot perform one of these actions on a single worker node in a set. You also cannot perform a single action on two sets of worker nodes (specified by two different worker node templates). ·        ·          Starting a set of worker nodes deploys a set of worker role instances in Windows Azure, which can take some time to complete, depending on the number of worker nodes and the performance of Windows Azure. ·        To start worker nodes manually and bring them online o   In HPC Node Management > Navigation Pane > Nodes > List / Heat Map view - select one or more worker nodes. o   Actions pane > Start – in the Start Azure Worker Nodes dialog, select a node template. o   the state of the worker nodes changes from Not Deployed to track the provisioning progress – worker node Details Pane > Provisioning Log tab. o   If there were errors during the provisioning of one or more worker nodes, the state of those nodes is set to Unknown and the node health is set to Unapproved. To determine the reason for the failure, review the provisioning logs for the nodes. o   After a worker node starts successfully, the node state changes to Offline. To bring the nodes online, select the nodes that are in the Offline state > Bring Online. ·        Troubleshooting o   check node template. o   use telnet to test connectivity: telnet <ServiceName>.cloudapp.net 7999 o   check node status - Deployment status information appears in the service account information in the Windows Azure Portal - HPC queries this -  see  node status information for any failed nodes in HPC Node Management. ·        When role instances are deployed, file packages that were previously uploaded to the storage account using the hpcpack command are automatically installed. You can also upload file packages to storage after the worker nodes are started, and then manually install them on the worker nodes. see hpcpack (http://go.microsoft.com/fwlink/?LinkID=205514). ·        to remove a set of role instances in Windows Azure - stop the nodes by using HPC Cluster Manager (apply the Stop action). This deletes the role instances from the service and changes the state of the worker nodes in the HPC cluster to Not Deployed. ·        Each time that you start a set of worker nodes, two proxy role instances (size Small) are configured in Windows Azure to facilitate communication between HPC Cluster Manager and the worker nodes. The proxy role instances are not listed in HPC Cluster Manager after the worker nodes are added. However, the instances appear in the Windows Azure Portal. The proxy role instances incur charges in Windows Azure along with the worker node instances, and they count toward the quota of role instances in the subscription.

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  • 256 Windows Azure Worker Roles, Windows Kinect and a 90's Text-Based Ray-Tracer

    - by Alan Smith
    For a couple of years I have been demoing a simple render farm hosted in Windows Azure using worker roles and the Azure Storage service. At the start of the presentation I deploy an Azure application that uses 16 worker roles to render a 1,500 frame 3D ray-traced animation. At the end of the presentation, when the animation was complete, I would play the animation delete the Azure deployment. The standing joke with the audience was that it was that it was a “$2 demo”, as the compute charges for running the 16 instances for an hour was $1.92, factor in the bandwidth charges and it’s a couple of dollars. The point of the demo is that it highlights one of the great benefits of cloud computing, you pay for what you use, and if you need massive compute power for a short period of time using Windows Azure can work out very cost effective. The “$2 demo” was great for presenting at user groups and conferences in that it could be deployed to Azure, used to render an animation, and then removed in a one hour session. I have always had the idea of doing something a bit more impressive with the demo, and scaling it from a “$2 demo” to a “$30 demo”. The challenge was to create a visually appealing animation in high definition format and keep the demo time down to one hour.  This article will take a run through how I achieved this. Ray Tracing Ray tracing, a technique for generating high quality photorealistic images, gained popularity in the 90’s with companies like Pixar creating feature length computer animations, and also the emergence of shareware text-based ray tracers that could run on a home PC. In order to render a ray traced image, the ray of light that would pass from the view point must be tracked until it intersects with an object. At the intersection, the color, reflectiveness, transparency, and refractive index of the object are used to calculate if the ray will be reflected or refracted. Each pixel may require thousands of calculations to determine what color it will be in the rendered image. Pin-Board Toys Having very little artistic talent and a basic understanding of maths I decided to focus on an animation that could be modeled fairly easily and would look visually impressive. I’ve always liked the pin-board desktop toys that become popular in the 80’s and when I was working as a 3D animator back in the 90’s I always had the idea of creating a 3D ray-traced animation of a pin-board, but never found the energy to do it. Even if I had a go at it, the render time to produce an animation that would look respectable on a 486 would have been measured in months. PolyRay Back in 1995 I landed my first real job, after spending three years being a beach-ski-climbing-paragliding-bum, and was employed to create 3D ray-traced animations for a CD-ROM that school kids would use to learn physics. I had got into the strange and wonderful world of text-based ray tracing, and was using a shareware ray-tracer called PolyRay. PolyRay takes a text file describing a scene as input and, after a few hours processing on a 486, produced a high quality ray-traced image. The following is an example of a basic PolyRay scene file. background Midnight_Blue   static define matte surface { ambient 0.1 diffuse 0.7 } define matte_white texture { matte { color white } } define matte_black texture { matte { color dark_slate_gray } } define position_cylindrical 3 define lookup_sawtooth 1 define light_wood <0.6, 0.24, 0.1> define median_wood <0.3, 0.12, 0.03> define dark_wood <0.05, 0.01, 0.005>     define wooden texture { noise surface { ambient 0.2  diffuse 0.7  specular white, 0.5 microfacet Reitz 10 position_fn position_cylindrical position_scale 1  lookup_fn lookup_sawtooth octaves 1 turbulence 1 color_map( [0.0, 0.2, light_wood, light_wood] [0.2, 0.3, light_wood, median_wood] [0.3, 0.4, median_wood, light_wood] [0.4, 0.7, light_wood, light_wood] [0.7, 0.8, light_wood, median_wood] [0.8, 0.9, median_wood, light_wood] [0.9, 1.0, light_wood, dark_wood]) } } define glass texture { surface { ambient 0 diffuse 0 specular 0.2 reflection white, 0.1 transmission white, 1, 1.5 }} define shiny surface { ambient 0.1 diffuse 0.6 specular white, 0.6 microfacet Phong 7  } define steely_blue texture { shiny { color black } } define chrome texture { surface { color white ambient 0.0 diffuse 0.2 specular 0.4 microfacet Phong 10 reflection 0.8 } }   viewpoint {     from <4.000, -1.000, 1.000> at <0.000, 0.000, 0.000> up <0, 1, 0> angle 60     resolution 640, 480 aspect 1.6 image_format 0 }       light <-10, 30, 20> light <-10, 30, -20>   object { disc <0, -2, 0>, <0, 1, 0>, 30 wooden }   object { sphere <0.000, 0.000, 0.000>, 1.00 chrome } object { cylinder <0.000, 0.000, 0.000>, <0.000, 0.000, -4.000>, 0.50 chrome }   After setting up the background and defining colors and textures, the viewpoint is specified. The “camera” is located at a point in 3D space, and it looks towards another point. The angle, image resolution, and aspect ratio are specified. Two lights are present in the image at defined coordinates. The three objects in the image are a wooden disc to represent a table top, and a sphere and cylinder that intersect to form a pin that will be used for the pin board toy in the final animation. When the image is rendered, the following image is produced. The pins are modeled with a chrome surface, so they reflect the environment around them. Note that the scale of the pin shaft is not correct, this will be fixed later. Modeling the Pin Board The frame of the pin-board is made up of three boxes, and six cylinders, the front box is modeled using a clear, slightly reflective solid, with the same refractive index of glass. The other shapes are modeled as metal. object { box <-5.5, -1.5, 1>, <5.5, 5.5, 1.2> glass } object { box <-5.5, -1.5, -0.04>, <5.5, 5.5, -0.09> steely_blue } object { box <-5.5, -1.5, -0.52>, <5.5, 5.5, -0.59> steely_blue } object { cylinder <-5.2, -1.2, 1.4>, <-5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, -1.2, 1.4>, <5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <-5.2, 5.2, 1.4>, <-5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, 5.2, 1.4>, <5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <0, -1.2, 1.4>, <0, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <0, 5.2, 1.4>, <0, 5.2, -0.74>, 0.2 steely_blue }   In order to create the matrix of pins that make up the pin board I used a basic console application with a few nested loops to create two intersecting matrixes of pins, which models the layout used in the pin boards. The resulting image is shown below. The pin board contains 11,481 pins, with the scene file containing 23,709 lines of code. For the complete animation 2,000 scene files will be created, which is over 47 million lines of code. Each pin in the pin-board will slide out a specific distance when an object is pressed into the back of the board. This is easily modeled by setting the Z coordinate of the pin to a specific value. In order to set all of the pins in the pin-board to the correct position, a bitmap image can be used. The position of the pin can be set based on the color of the pixel at the appropriate position in the image. When the Windows Azure logo is used to set the Z coordinate of the pins, the following image is generated. The challenge now was to make a cool animation. The Azure Logo is fine, but it is static. Using a normal video to animate the pins would not work; the colors in the video would not be the same as the depth of the objects from the camera. In order to simulate the pin board accurately a series of frames from a depth camera could be used. Windows Kinect The Kenect controllers for the X-Box 360 and Windows feature a depth camera. The Kinect SDK for Windows provides a programming interface for Kenect, providing easy access for .NET developers to the Kinect sensors. The Kinect Explorer provided with the Kinect SDK is a great starting point for exploring Kinect from a developers perspective. Both the X-Box 360 Kinect and the Windows Kinect will work with the Kinect SDK, the Windows Kinect is required for commercial applications, but the X-Box Kinect can be used for hobby projects. The Windows Kinect has the advantage of providing a mode to allow depth capture with objects closer to the camera, which makes for a more accurate depth image for setting the pin positions. Creating a Depth Field Animation The depth field animation used to set the positions of the pin in the pin board was created using a modified version of the Kinect Explorer sample application. In order to simulate the pin board accurately, a small section of the depth range from the depth sensor will be used. Any part of the object in front of the depth range will result in a white pixel; anything behind the depth range will be black. Within the depth range the pixels in the image will be set to RGB values from 0,0,0 to 255,255,255. A screen shot of the modified Kinect Explorer application is shown below. The Kinect Explorer sample application was modified to include slider controls that are used to set the depth range that forms the image from the depth stream. This allows the fine tuning of the depth image that is required for simulating the position of the pins in the pin board. The Kinect Explorer was also modified to record a series of images from the depth camera and save them as a sequence JPEG files that will be used to animate the pins in the animation the Start and Stop buttons are used to start and stop the image recording. En example of one of the depth images is shown below. Once a series of 2,000 depth images has been captured, the task of creating the animation can begin. Rendering a Test Frame In order to test the creation of frames and get an approximation of the time required to render each frame a test frame was rendered on-premise using PolyRay. The output of the rendering process is shown below. The test frame contained 23,629 primitive shapes, most of which are the spheres and cylinders that are used for the 11,800 or so pins in the pin board. The 1280x720 image contains 921,600 pixels, but as anti-aliasing was used the number of rays that were calculated was 4,235,777, with 3,478,754,073 object boundaries checked. The test frame of the pin board with the depth field image applied is shown below. The tracing time for the test frame was 4 minutes 27 seconds, which means rendering the2,000 frames in the animation would take over 148 hours, or a little over 6 days. Although this is much faster that an old 486, waiting almost a week to see the results of an animation would make it challenging for animators to create, view, and refine their animations. It would be much better if the animation could be rendered in less than one hour. Windows Azure Worker Roles The cost of creating an on-premise render farm to render animations increases in proportion to the number of servers. The table below shows the cost of servers for creating a render farm, assuming a cost of $500 per server. Number of Servers Cost 1 $500 16 $8,000 256 $128,000   As well as the cost of the servers, there would be additional costs for networking, racks etc. Hosting an environment of 256 servers on-premise would require a server room with cooling, and some pretty hefty power cabling. The Windows Azure compute services provide worker roles, which are ideal for performing processor intensive compute tasks. With the scalability available in Windows Azure a job that takes 256 hours to complete could be perfumed using different numbers of worker roles. The time and cost of using 1, 16 or 256 worker roles is shown below. Number of Worker Roles Render Time Cost 1 256 hours $30.72 16 16 hours $30.72 256 1 hour $30.72   Using worker roles in Windows Azure provides the same cost for the 256 hour job, irrespective of the number of worker roles used. Provided the compute task can be broken down into many small units, and the worker role compute power can be used effectively, it makes sense to scale the application so that the task is completed quickly, making the results available in a timely fashion. The task of rendering 2,000 frames in an animation is one that can easily be broken down into 2,000 individual pieces, which can be performed by a number of worker roles. Creating a Render Farm in Windows Azure The architecture of the render farm is shown in the following diagram. The render farm is a hybrid application with the following components: ·         On-Premise o   Windows Kinect – Used combined with the Kinect Explorer to create a stream of depth images. o   Animation Creator – This application uses the depth images from the Kinect sensor to create scene description files for PolyRay. These files are then uploaded to the jobs blob container, and job messages added to the jobs queue. o   Process Monitor – This application queries the role instance lifecycle table and displays statistics about the render farm environment and render process. o   Image Downloader – This application polls the image queue and downloads the rendered animation files once they are complete. ·         Windows Azure o   Azure Storage – Queues and blobs are used for the scene description files and completed frames. A table is used to store the statistics about the rendering environment.   The architecture of each worker role is shown below.   The worker role is configured to use local storage, which provides file storage on the worker role instance that can be use by the applications to render the image and transform the format of the image. The service definition for the worker role with the local storage configuration highlighted is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceDefinition name="CloudRay" >   <WorkerRole name="CloudRayWorkerRole" vmsize="Small">     <Imports>     </Imports>     <ConfigurationSettings>       <Setting name="DataConnectionString" />     </ConfigurationSettings>     <LocalResources>       <LocalStorage name="RayFolder" cleanOnRoleRecycle="true" />     </LocalResources>   </WorkerRole> </ServiceDefinition>     The two executable programs, PolyRay.exe and DTA.exe are included in the Azure project, with Copy Always set as the property. PolyRay will take the scene description file and render it to a Truevision TGA file. As the TGA format has not seen much use since the mid 90’s it is converted to a JPG image using Dave's Targa Animator, another shareware application from the 90’s. Each worker roll will use the following process to render the animation frames. 1.       The worker process polls the job queue, if a job is available the scene description file is downloaded from blob storage to local storage. 2.       PolyRay.exe is started in a process with the appropriate command line arguments to render the image as a TGA file. 3.       DTA.exe is started in a process with the appropriate command line arguments convert the TGA file to a JPG file. 4.       The JPG file is uploaded from local storage to the images blob container. 5.       A message is placed on the images queue to indicate a new image is available for download. 6.       The job message is deleted from the job queue. 7.       The role instance lifecycle table is updated with statistics on the number of frames rendered by the worker role instance, and the CPU time used. The code for this is shown below. public override void Run() {     // Set environment variables     string polyRayPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), PolyRayLocation);     string dtaPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), DTALocation);       LocalResource rayStorage = RoleEnvironment.GetLocalResource("RayFolder");     string localStorageRootPath = rayStorage.RootPath;       JobQueue jobQueue = new JobQueue("renderjobs");     JobQueue downloadQueue = new JobQueue("renderimagedownloadjobs");     CloudRayBlob sceneBlob = new CloudRayBlob("scenes");     CloudRayBlob imageBlob = new CloudRayBlob("images");     RoleLifecycleDataSource roleLifecycleDataSource = new RoleLifecycleDataSource();       Frames = 0;       while (true)     {         // Get the render job from the queue         CloudQueueMessage jobMsg = jobQueue.Get();           if (jobMsg != null)         {             // Get the file details             string sceneFile = jobMsg.AsString;             string tgaFile = sceneFile.Replace(".pi", ".tga");             string jpgFile = sceneFile.Replace(".pi", ".jpg");               string sceneFilePath = Path.Combine(localStorageRootPath, sceneFile);             string tgaFilePath = Path.Combine(localStorageRootPath, tgaFile);             string jpgFilePath = Path.Combine(localStorageRootPath, jpgFile);               // Copy the scene file to local storage             sceneBlob.DownloadFile(sceneFilePath);               // Run the ray tracer.             string polyrayArguments =                 string.Format("\"{0}\" -o \"{1}\" -a 2", sceneFilePath, tgaFilePath);             Process polyRayProcess = new Process();             polyRayProcess.StartInfo.FileName =                 Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), polyRayPath);             polyRayProcess.StartInfo.Arguments = polyrayArguments;             polyRayProcess.Start();             polyRayProcess.WaitForExit();               // Convert the image             string dtaArguments =                 string.Format(" {0} /FJ /P{1}", tgaFilePath, Path.GetDirectoryName (jpgFilePath));             Process dtaProcess = new Process();             dtaProcess.StartInfo.FileName =                 Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), dtaPath);             dtaProcess.StartInfo.Arguments = dtaArguments;             dtaProcess.Start();             dtaProcess.WaitForExit();               // Upload the image to blob storage             imageBlob.UploadFile(jpgFilePath);               // Add a download job.             downloadQueue.Add(jpgFile);               // Delete the render job message             jobQueue.Delete(jobMsg);               Frames++;         }         else         {             Thread.Sleep(1000);         }           // Log the worker role activity.         roleLifecycleDataSource.Alive             ("CloudRayWorker", RoleLifecycleDataSource.RoleLifecycleId, Frames);     } }     Monitoring Worker Role Instance Lifecycle In order to get more accurate statistics about the lifecycle of the worker role instances used to render the animation data was tracked in an Azure storage table. The following class was used to track the worker role lifecycles in Azure storage.   public class RoleLifecycle : TableServiceEntity {     public string ServerName { get; set; }     public string Status { get; set; }     public DateTime StartTime { get; set; }     public DateTime EndTime { get; set; }     public long SecondsRunning { get; set; }     public DateTime LastActiveTime { get; set; }     public int Frames { get; set; }     public string Comment { get; set; }       public RoleLifecycle()     {     }       public RoleLifecycle(string roleName)     {         PartitionKey = roleName;         RowKey = Utils.GetAscendingRowKey();         Status = "Started";         StartTime = DateTime.UtcNow;         LastActiveTime = StartTime;         EndTime = StartTime;         SecondsRunning = 0;         Frames = 0;     } }     A new instance of this class is created and added to the storage table when the role starts. It is then updated each time the worker renders a frame to record the total number of frames rendered and the total processing time. These statistics are used be the monitoring application to determine the effectiveness of use of resources in the render farm. Rendering the Animation The Azure solution was deployed to Windows Azure with the service configuration set to 16 worker role instances. This allows for the application to be tested in the cloud environment, and the performance of the application determined. When I demo the application at conferences and user groups I often start with 16 instances, and then scale up the application to the full 256 instances. The configuration to run 16 instances is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*">   <Role name="CloudRayWorkerRole">     <Instances count="16" />     <ConfigurationSettings>       <Setting name="DataConnectionString"         value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." />     </ConfigurationSettings>   </Role> </ServiceConfiguration>     About six minutes after deploying the application the first worker roles become active and start to render the first frames of the animation. The CloudRay Monitor application displays an icon for each worker role instance, with a number indicating the number of frames that the worker role has rendered. The statistics on the left show the number of active worker roles and statistics about the render process. The render time is the time since the first worker role became active; the CPU time is the total amount of processing time used by all worker role instances to render the frames.   Five minutes after the first worker role became active the last of the 16 worker roles activated. By this time the first seven worker roles had each rendered one frame of the animation.   With 16 worker roles u and running it can be seen that one hour and 45 minutes CPU time has been used to render 32 frames with a render time of just under 10 minutes.     At this rate it would take over 10 hours to render the 2,000 frames of the full animation. In order to complete the animation in under an hour more processing power will be required. Scaling the render farm from 16 instances to 256 instances is easy using the new management portal. The slider is set to 256 instances, and the configuration saved. We do not need to re-deploy the application, and the 16 instances that are up and running will not be affected. Alternatively, the configuration file for the Azure service could be modified to specify 256 instances.   <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*">   <Role name="CloudRayWorkerRole">     <Instances count="256" />     <ConfigurationSettings>       <Setting name="DataConnectionString"         value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." />     </ConfigurationSettings>   </Role> </ServiceConfiguration>     Six minutes after the new configuration has been applied 75 new worker roles have activated and are processing their first frames.   Five minutes later the full configuration of 256 worker roles is up and running. We can see that the average rate of frame rendering has increased from 3 to 12 frames per minute, and that over 17 hours of CPU time has been utilized in 23 minutes. In this test the time to provision 140 worker roles was about 11 minutes, which works out at about one every five seconds.   We are now half way through the rendering, with 1,000 frames complete. This has utilized just under three days of CPU time in a little over 35 minutes.   The animation is now complete, with 2,000 frames rendered in a little over 52 minutes. The CPU time used by the 256 worker roles is 6 days, 7 hours and 22 minutes with an average frame rate of 38 frames per minute. The rendering of the last 1,000 frames took 16 minutes 27 seconds, which works out at a rendering rate of 60 frames per minute. The frame counts in the server instances indicate that the use of a queue to distribute the workload has been very effective in distributing the load across the 256 worker role instances. The first 16 instances that were deployed first have rendered between 11 and 13 frames each, whilst the 240 instances that were added when the application was scaled have rendered between 6 and 9 frames each.   Completed Animation I’ve uploaded the completed animation to YouTube, a low resolution preview is shown below. Pin Board Animation Created using Windows Kinect and 256 Windows Azure Worker Roles   The animation can be viewed in 1280x720 resolution at the following link: http://www.youtube.com/watch?v=n5jy6bvSxWc Effective Use of Resources According to the CloudRay monitor statistics the animation took 6 days, 7 hours and 22 minutes CPU to render, this works out at 152 hours of compute time, rounded up to the nearest hour. As the usage for the worker role instances are billed for the full hour, it may have been possible to render the animation using fewer than 256 worker roles. When deciding the optimal usage of resources, the time required to provision and start the worker roles must also be considered. In the demo I started with 16 worker roles, and then scaled the application to 256 worker roles. It would have been more optimal to start the application with maybe 200 worker roles, and utilized the full hour that I was being billed for. This would, however, have prevented showing the ease of scalability of the application. The new management portal displays the CPU usage across the worker roles in the deployment. The average CPU usage across all instances is 93.27%, with over 99% used when all the instances are up and running. This shows that the worker role resources are being used very effectively. Grid Computing Scenarios Although I am using this scenario for a hobby project, there are many scenarios where a large amount of compute power is required for a short period of time. Windows Azure provides a great platform for developing these types of grid computing applications, and can work out very cost effective. ·         Windows Azure can provide massive compute power, on demand, in a matter of minutes. ·         The use of queues to manage the load balancing of jobs between role instances is a simple and effective solution. ·         Using a cloud-computing platform like Windows Azure allows proof-of-concept scenarios to be tested and evaluated on a very low budget. ·         No charges for inbound data transfer makes the uploading of large data sets to Windows Azure Storage services cost effective. (Transaction charges still apply.) Tips for using Windows Azure for Grid Computing Scenarios I found the implementation of a render farm using Windows Azure a fairly simple scenario to implement. I was impressed by ease of scalability that Azure provides, and by the short time that the application took to scale from 16 to 256 worker role instances. In this case it was around 13 minutes, in other tests it took between 10 and 20 minutes. The following tips may be useful when implementing a grid computing project in Windows Azure. ·         Using an Azure Storage queue to load-balance the units of work across multiple worker roles is simple and very effective. The design I have used in this scenario could easily scale to many thousands of worker role instances. ·         Windows Azure accounts are typically limited to 20 cores. If you need to use more than this, a call to support and a credit card check will be required. ·         Be aware of how the billing model works. You will be charged for worker role instances for the full clock our in which the instance is deployed. Schedule the workload to start just after the clock hour has started. ·         Monitor the utilization of the resources you are provisioning, ensure that you are not paying for worker roles that are idle. ·         If you are deploying third party applications to worker roles, you may well run into licensing issues. Purchasing software licenses on a per-processor basis when using hundreds of processors for a short time period would not be cost effective. ·         Third party software may also require installation onto the worker roles, which can be accomplished using start-up tasks. Bear in mind that adding a startup task and possible re-boot will add to the time required for the worker role instance to start and activate. An alternative may be to use a prepared VM and use VM roles. ·         Consider using the Windows Azure Autoscaling Application Block (WASABi) to autoscale the worker roles in your application. When using a large number of worker roles, the utilization must be carefully monitored, if the scaling algorithms are not optimal it could get very expensive!

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  • apache fails to connect to tomcat (Worker config?)

    - by techventure
    I have a tomcat 6 with follwoing server.xml: <Connector port="8253" maxThreads="150" minSpareThreads="25" maxSpareThreads="75" enableLookups="false" redirectPort="8445" acceptCount="100" debug="0" connectionTimeout="20000" disableUploadTimeout="true" /> <Connector port="8014" protocol="AJP/1.3" redirectPort="8445" /> and in added worker.properties: # Set properties for worker4 (ajp13) worker.worker4.type=ajp13 worker.worker4.host=localhost worker.worker4.port=8014 and i put in httpd.conf: JkMount /myWebApp/* worker4 It is not working a as trying to navigate to www1.myCompany.com/myWebApp gives "Service Temporarily Unavailable". I checked in tomcat catalina.out and it says: INFO: JK: ajp13 listening on /0.0.0.0:8014 UPDATE: i put mod_jk log level to debug and below is the result: [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_set_time_fmt::jk_util.c (458): Pre-processed log time stamp format is '[%a %b %d %H:%M:%S %Y] ' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_open::jk_uri_worker_map.c (770): rule map size is 8 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_add::jk_uri_worker_map.c (720): wildchar rule '/myWebApp/*=worker4' source 'JkMount' was added [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (171): uri map dump after map open: index=0 file='(null)' reject_unsafe=0 reload=60 modified=0 checked=0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (176): generation 0: size=0 nosize=0 capacity=0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (176): generation 1: size=8 nosize=0 capacity=8 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (186): NEXT (1) map #3: uri=/myWebApp/* worker=worker4 context=/myWebApp/* source=JkMount type=Wildchar len=6 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_set_time_fmt::jk_util.c (458): Pre-processed log time stamp format is '[%a %b %d %H:%M:%S %Y] ' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] init_jk::mod_jk.c (3123): Setting default connection pool max size to 1 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_read_property::jk_map.c (491): Adding property 'worker.list' with value 'worker1,worker2,worker3,worker4' to map. [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_read_property::jk_map.c (491): Adding property 'worker.worker4.type' with value 'ajp13' to map. [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_read_property::jk_map.c (491): Adding property 'worker.worker4.host' with value 'localhost' to map. [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_read_property::jk_map.c (491): Adding property 'worker.worker4.port' with value '8014' to map. [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_resolve_references::jk_map.c (774): Checking for references with prefix worker. with wildcard (recursion 1) [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_shm_calculate_size::jk_shm.c (132): shared memory will contain 4 ajp workers of size 256 and 0 lb workers of size 320 with 0 members of size 320+256 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [error] init_jk::mod_jk.c (3166): Initializing shm:/var/log/httpd/mod_jk.shm.9552 errno=13. Load balancing workers will not function properly. [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'ServerRoot' -> '/etc/httpd' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.list' -> 'worker1,worker2,worker3,worker4' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker1.type' -> 'ajp13' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker1.host' -> 'localhost' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker1.port' -> '8009' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker2.type' -> 'ajp13' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker2.host' -> 'localhost' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker2.port' -> '8010' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker3.type' -> 'ajp13' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker3.host' -> 'localhost' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker3.port' -> '8112' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker4.type' -> 'ajp13' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker4.host' -> 'localhost' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker4.port' -> '8014' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] build_worker_map::jk_worker.c (242): creating worker worker4 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] wc_create_worker::jk_worker.c (146): about to create instance worker4 of ajp13 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] wc_create_worker::jk_worker.c (159): about to validate and init worker4 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_validate::jk_ajp_common.c (2512): worker worker4 contact is 'localhost:8014' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2699): setting endpoint options: [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2702): keepalive: 0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2706): socket timeout: 0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2710): socket connect timeout: 0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2714): buffer size: 0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2718): pool timeout: 0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2722): ping timeout: 10000 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2726): connect timeout: 0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2730): reply timeout: 0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2734): prepost timeout: 0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2738): recovery options: 0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2742): retries: 2 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2746): max packet size: 8192 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_init::jk_ajp_common.c (2750): retry interval: 100 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] ajp_create_endpoint_cache::jk_ajp_common.c (2562): setting connection pool size to 1 with min 1 and acquire timeout 200 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [info] init_jk::mod_jk.c (3183): mod_jk/1.2.28 initialized [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] wc_get_worker_for_name::jk_worker.c (116): found a worker worker4 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] wc_get_name_for_type::jk_worker.c (293): Found worker type 'ajp13' [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_ext::jk_uri_worker_map.c (512): Checking extension for worker 3: worker4 of type ajp13 (2) [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (171): uri map dump after extension stripping: index=0 file='(null)' reject_unsafe=0 reload=60 modified=0 checked=0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (176): generation 0: size=0 nosize=0 capacity=0 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (176): generation 1: size=8 nosize=0 capacity=8 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (186): NEXT (1) map #3: uri=/myWebApp/* worker=worker4 context=/myWebApp/* source=JkMount type=Wildchar len=6 [Wed Jun 13 18:44:26 2012] [9552:3086317328] [debug] uri_worker_map_switch::jk_uri_worker_map.c (482): Switching uri worker map from index 0 to index 1 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_set_time_fmt::jk_util.c (458): Pre-processed log time stamp format is '[%a %b %d %H:%M:%S %Y] ' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_open::jk_uri_worker_map.c (770): rule map size is 8 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_add::jk_uri_worker_map.c (720): wildchar rule '/myWebApp/*=worker4' source 'JkMount' was added [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (171): uri map dump after map open: index=0 file='(null)' reject_unsafe=0 reload=60 modified=0 checked=0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (176): generation 0: size=0 nosize=0 capacity=0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (176): generation 1: size=8 nosize=0 capacity=8 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (186): NEXT (1) map #0: uri=/jsp-examples/* worker=worker1 context=/jsp-examples/* source=JkMount type=Wildchar len=15 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (186): NEXT (1) map #3: uri=/myWebApp/* worker=worker4 context=/myWebApp/* source=JkMount type=Wildchar len=6 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_set_time_fmt::jk_util.c (458): Pre-processed log time stamp format is '[%a %b %d %H:%M:%S %Y] ' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] init_jk::mod_jk.c (3123): Setting default connection pool max size to 1 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_read_property::jk_map.c (491): Adding property 'worker.list' with value 'worker1,worker2,worker3,worker4' to map. [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_read_property::jk_map.c (491): Adding property 'worker.worker4.type' with value 'ajp13' to map. [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_read_property::jk_map.c (491): Adding property 'worker.worker4.host' with value 'localhost' to map. [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_read_property::jk_map.c (491): Adding property 'worker.worker4.port' with value '8014' to map. [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_resolve_references::jk_map.c (774): Checking for references with prefix worker. with wildcard (recursion 1) [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_shm_calculate_size::jk_shm.c (132): shared memory will contain 4 ajp workers of size 256 and 0 lb workers of size 320 with 0 members of size 320+256 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [error] init_jk::mod_jk.c (3166): Initializing shm:/var/log/httpd/mod_jk.shm.9553 errno=13. Load balancing workers will not function properly. [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'ServerRoot' -> '/etc/httpd' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.list' -> 'worker1,worker2,worker3,worker4' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker1.type' -> 'ajp13' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker1.host' -> 'localhost' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker1.port' -> '8009' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker2.type' -> 'ajp13' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker2.host' -> 'localhost' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker2.port' -> '8010' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker3.type' -> 'ajp13' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker3.host' -> 'localhost' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker3.port' -> '8112' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker4.type' -> 'ajp13' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker4.host' -> 'localhost' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] jk_map_dump::jk_map.c (589): Dump of map: 'worker.worker4.port' -> '8014' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] build_worker_map::jk_worker.c (242): creating worker worker4 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] wc_create_worker::jk_worker.c (146): about to create instance worker4 of ajp13 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] wc_create_worker::jk_worker.c (159): about to validate and init worker4 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_validate::jk_ajp_common.c (2512): worker worker4 contact is 'localhost:8014' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2699): setting endpoint options: [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2702): keepalive: 0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2706): socket timeout: 0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2710): socket connect timeout: 0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2714): buffer size: 0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2718): pool timeout: 0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2722): ping timeout: 10000 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2726): connect timeout: 0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2730): reply timeout: 0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2734): prepost timeout: 0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2738): recovery options: 0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2742): retries: 2 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2746): max packet size: 8192 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_init::jk_ajp_common.c (2750): retry interval: 100 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] ajp_create_endpoint_cache::jk_ajp_common.c (2562): setting connection pool size to 1 with min 1 and acquire timeout 200 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [info] init_jk::mod_jk.c (3183): mod_jk/1.2.28 initialized [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] wc_get_worker_for_name::jk_worker.c (116): found a worker worker4 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] wc_get_name_for_type::jk_worker.c (293): Found worker type 'ajp13' [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_ext::jk_uri_worker_map.c (512): Checking extension for worker 3: worker4 of type ajp13 (2) [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (171): uri map dump after extension stripping: index=0 file='(null)' reject_unsafe=0 reload=60 modified=0 checked=0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (176): generation 0: size=0 nosize=0 capacity=0 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (176): generation 1: size=8 nosize=0 capacity=8 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_dump::jk_uri_worker_map.c (186): NEXT (1) map #3: uri=/myWebApp/* worker=worker4 context=/myWebApp/* source=JkMount type=Wildchar len=6 [Wed Jun 13 18:44:26 2012] [9553:3086317328] [debug] uri_worker_map_switch::jk_uri_worker_map.c (482): Switching uri worker map from index 0 to index 1 [Wed Jun 13 18:44:26 2012] [9555:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9556:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9557:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9558:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9559:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9560:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9561:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9562:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9563:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9564:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9565:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9567:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9568:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9566:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9569:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:26 2012] [9570:3086317328] [debug] jk_child_init::mod_jk.c (3068): Initialized mod_jk/1.2.28 [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] map_uri_to_worker_ext::jk_uri_worker_map.c (1036): Attempting to map URI '/myWebApp/jsp/login.faces' from 8 maps [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] find_match::jk_uri_worker_map.c (850): Attempting to map context URI '/myWebApp/*=worker4' source 'JkMount' [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] find_match::jk_uri_worker_map.c (863): Found a wildchar match '/myWebApp/*=worker4' [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] jk_handler::mod_jk.c (2459): Into handler jakarta-servlet worker=worker4 r->proxyreq=0 [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] wc_get_worker_for_name::jk_worker.c (116): found a worker worker4 [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] wc_maintain::jk_worker.c (339): Maintaining worker worker1 [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] wc_maintain::jk_worker.c (339): Maintaining worker worker2 [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] wc_maintain::jk_worker.c (339): Maintaining worker worker3 [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] wc_maintain::jk_worker.c (339): Maintaining worker worker4 [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] wc_get_name_for_type::jk_worker.c (293): Found worker type 'ajp13' [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] init_ws_service::mod_jk.c (977): Service protocol=HTTP/1.1 method=GET ssl=false host=(null) addr=167.184.214.6 name=www1.myCompany.com.au port=80 auth=(null) user=(null) laddr=10.215.222.78 raddr=167.184.214.6 uri=/myWebApp/jsp/login.faces [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] ajp_get_endpoint::jk_ajp_common.c (2977): acquired connection pool slot=0 after 0 retries [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] ajp_marshal_into_msgb::jk_ajp_common.c (605): ajp marshaling done [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] ajp_service::jk_ajp_common.c (2283): processing worker4 with 2 retries [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] ajp_send_request::jk_ajp_common.c (1501): (worker4) all endpoints are disconnected. [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] jk_open_socket::jk_connect.c (452): socket TCP_NODELAY set to On [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] jk_open_socket::jk_connect.c (576): trying to connect socket 18 to 127.0.0.1:8014 [Wed Jun 13 18:44:54 2012] [9555:3086317328] [info] jk_open_socket::jk_connect.c (594): connect to 127.0.0.1:8014 failed (errno=13) [Wed Jun 13 18:44:54 2012] [9555:3086317328] [info] ajp_connect_to_endpoint::jk_ajp_common.c (922): Failed opening socket to (127.0.0.1:8014) (errno=13) [Wed Jun 13 18:44:54 2012] [9555:3086317328] [error] ajp_send_request::jk_ajp_common.c (1507): (worker4) connecting to backend failed. Tomcat is probably not started or is listening on the wrong port (errno=13) [Wed Jun 13 18:44:54 2012] [9555:3086317328] [info] ajp_service::jk_ajp_common.c (2447): (worker4) sending request to tomcat failed (recoverable), because of error during request sending (attempt=1) [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] ajp_service::jk_ajp_common.c (2304): retry 1, sleeping for 100 ms before retrying [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] ajp_send_request::jk_ajp_common.c (1501): (worker4) all endpoints are disconnected. [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] jk_open_socket::jk_connect.c (452): socket TCP_NODELAY set to On [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] jk_open_socket::jk_connect.c (576): trying to connect socket 18 to 127.0.0.1:8014 [Wed Jun 13 18:44:54 2012] [9555:3086317328] [info] jk_open_socket::jk_connect.c (594): connect to 127.0.0.1:8014 failed (errno=13) [Wed Jun 13 18:44:54 2012] [9555:3086317328] [info] ajp_connect_to_endpoint::jk_ajp_common.c (922): Failed opening socket to (127.0.0.1:8014) (errno=13) [Wed Jun 13 18:44:54 2012] [9555:3086317328] [error] ajp_send_request::jk_ajp_common.c (1507): (worker4) connecting to backend failed. Tomcat is probably not started or is listening on the wrong port (errno=13) [Wed Jun 13 18:44:54 2012] [9555:3086317328] [info] ajp_service::jk_ajp_common.c (2447): (worker4) sending request to tomcat failed (recoverable), because of error during request sending (attempt=2) [Wed Jun 13 18:44:54 2012] [9555:3086317328] [error] ajp_service::jk_ajp_common.c (2466): (worker4) connecting to tomcat failed. [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] ajp_reset_endpoint::jk_ajp_common.c (743): (worker4) resetting endpoint with sd = 4294967295 (socket shutdown) [Wed Jun 13 18:44:54 2012] [9555:3086317328] [debug] ajp_done::jk_ajp_common.c (2905): recycling connection pool slot=0 for worker worker4 [Wed Jun 13 18:44:54 2012] [9555:3086317328] [info] jk_handler::mod_jk.c (2615): Service error=-3 for worker=worker4 The error i get in browser is: Service Temporarily Unavailable Apache/2.2.3 (Red Hat) Server at www1.myCompany.com.au Port 80 can someone please help and explain what is going on and how it can be resolved?

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  • Large concurrent user performance issues for Apache + mod_jk + GlassFish v3.1 clusters

    - by user10035
    I am running a java ee 6 ear application on a GlassFish v3.1 ( 2 clusters with 2 instances each) load balanced by an Apache v2.2 with mod_jk - all on the same server (Windows Server 2003 R2, Intel Xeon CPU x5670 @2.93Ghz, 6GB RAM, 2 cpus). The web application is accessed by around ~100 users. When they all try to access it at the same time every morning ~8am, the response is very slow while trying to access the main jsf home page. Apart from that I have seen the CPU usage spike upto 99% by the httpd process during the day frequently and I start seeing errors in the mod_jk.log file. [Wed Jun 08 08:25:43 2011] [9380:8216] [info] ajp_process_callback::jk_ajp_common.c (1885): Writing to client aborted or client network problems [Wed Jun 08 08:25:43 2011] [9380:8216] [info] ajp_service::jk_ajp_common.c (2543): (myAppLocalInstance4) sending request to tomcat failed (unrecoverable), because of client write error (attempt=1) Any suggestions on how I can go about improving this? Apache configuration is mostly the default as shown below ServerRoot "C:/Program Files/Apache Software Foundation/Apache2.2" Listen 80 LoadModule actions_module modules/mod_actions.so LoadModule alias_module modules/mod_alias.so LoadModule asis_module modules/mod_asis.so LoadModule auth_basic_module modules/mod_auth_basic.so LoadModule authn_default_module modules/mod_authn_default.so LoadModule authn_file_module modules/mod_authn_file.so LoadModule authz_default_module modules/mod_authz_default.so LoadModule authz_groupfile_module modules/mod_authz_groupfile.so LoadModule authz_host_module modules/mod_authz_host.so LoadModule authz_user_module modules/mod_authz_user.so LoadModule autoindex_module modules/mod_autoindex.so LoadModule cgi_module modules/mod_cgi.so LoadModule dir_module modules/mod_dir.so LoadModule env_module modules/mod_env.so LoadModule include_module modules/mod_include.so LoadModule isapi_module modules/mod_isapi.so LoadModule log_config_module modules/mod_log_config.so LoadModule mime_module modules/mod_mime.so LoadModule negotiation_module modules/mod_negotiation.so LoadModule setenvif_module modules/mod_setenvif.so <IfModule !mpm_netware_module> <IfModule !mpm_winnt_module> User daemon Group daemon </IfModule> </IfModule> DocumentRoot "C:/Program Files/Apache Software Foundation/Apache2.2/htdocs" <Directory /> Options FollowSymLinks AllowOverride None Order deny,allow Deny from all </Directory> <Directory "C:/Program Files/Apache Software Foundation/Apache2.2/htdocs"> Options Indexes FollowSymLinks AllowOverride None Order allow,deny Allow from all </Directory> <IfModule dir_module> DirectoryIndex index.html </IfModule> <FilesMatch "^\.ht"> Order allow,deny Deny from all Satisfy All </FilesMatch> ErrorLog "logs/error.log" LogLevel warn <IfModule log_config_module> LogFormat "%h %l %u %t \"%r\" %>s %b \"%{Referer}i\" \"%{User-Agent}i\"" combined LogFormat "%h %l %u %t \"%r\" %>s %b" common <IfModule logio_module> LogFormat "%h %l %u %t \"%r\" %>s %b \"%{Referer}i\" \"%{User-Agent}i\" %I %O" combinedio </IfModule> CustomLog "logs/access.log" common </IfModule> <IfModule alias_module> ScriptAlias /cgi-bin/ "C:/Program Files/Apache Software Foundation/Apache2.2/cgi-bin/" </IfModule> <Directory "C:/Program Files/Apache Software Foundation/Apache2.2/cgi-bin"> AllowOverride None Options None Order allow,deny Allow from all </Directory> DefaultType text/plain <IfModule mime_module> TypesConfig conf/mime.types AddType application/x-compress .Z AddType application/x-gzip .gz .tgz </IfModule> Include conf/extra/httpd-mpm.conf <IfModule ssl_module> SSLRandomSeed startup builtin SSLRandomSeed connect builtin </IfModule> LoadModule jk_module modules/mod_jk.so JkWorkersFile conf/workers.properties JkLogFile logs/mod_jk.log JkLogLevel info JkLogStampFormat "[%a %b %d %H:%M:%S %Y] " JkOptions +ForwardKeySize +ForwardURICompat -ForwardDirectories JkRequestLogFormat "%w %V %T" JkMount /myApp/* loadbalancerLocal JkMount /myAppRemote/* loadbalancerRemote JkMount /myApp loadbalancerLocal JkMount /myAppRemote loadbalancerRemote The workers.properties config file is: worker.list=loadbalancerLocal,loadbalancerRemote worker.myAppLocalInstance1.type=ajp13 worker.myAppLocalInstance1.host=localhost worker.myAppLocalInstance1.port=8109 worker.myAppLocalInstance1.lbfactor=1 worker.myAppLocalInstance1.socket_keepalive=1 worker.myAppLocalInstance1.socket_timeout=1000 worker.myAppLocalInstance2.type=ajp13 worker.myAppLocalInstance2.host=localhost worker.myAppLocalInstance2.port=8209 worker.myAppLocalInstance2.lbfactor=1 worker.myAppLocalInstance2.socket_keepalive=1 worker.myAppLocalInstance2.socket_timeout=1000 worker.myAppLocalInstance3.type=ajp13 worker.myAppLocalInstance3.host=localhost worker.myAppLocalInstance3.port=8309 worker.myAppLocalInstance3.lbfactor=1 worker.myAppLocalInstance3.socket_keepalive=1 worker.myAppLocalInstance3.socket_timeout=1000 worker.myAppLocalInstance4.type=ajp13 worker.myAppLocalInstance4.host=localhost worker.myAppLocalInstance4.port=8409 worker.myAppLocalInstance4.lbfactor=1 worker.myAppLocalInstance4.socket_keepalive=1 worker.myAppLocalInstance4.socket_timeout=1000 worker.myAppRemoteInstance1.type=ajp13 worker.myAppRemoteInstance1.host=localhost worker.myAppRemoteInstance1.port=8509 worker.myAppRemoteInstance1.lbfactor=1 worker.myAppRemoteInstance1.socket_keepalive=1 worker.myAppRemoteInstance1.socket_timeout=1000 worker.myAppRemoteInstance2.type=ajp13 worker.myAppRemoteInstance2.host=localhost worker.myAppRemoteInstance2.port=8609 worker.myAppRemoteInstance2.lbfactor=1 worker.myAppRemoteInstance2.socket_keepalive=1 worker.myAppRemoteInstance2.socket_timeout=1000 worker.myAppRemoteInstance3.type=ajp13 worker.myAppRemoteInstance3.host=localhost worker.myAppRemoteInstance3.port=8709 worker.myAppRemoteInstance3.lbfactor=1 worker.myAppRemoteInstance3.socket_keepalive=1 worker.myAppRemoteInstance3.socket_timeout=1000 worker.myAppRemoteInstance4.type=ajp13 worker.myAppRemoteInstance4.host=localhost worker.myAppRemoteInstance4.port=8809 worker.myAppRemoteInstance4.lbfactor=1 worker.myAppRemoteInstance4.socket_keepalive=1 worker.myAppRemoteInstance4.socket_timeout=1000 worker.loadbalancerLocal.type=lb worker.loadbalancerLocal.sticky_session=True worker.loadbalancerLocal.balance_workers=myAppLocalInstance1,myAppLocalInstance2,myAppLocalInstance3,myAppLocalInstance4 worker.loadbalancerRemote.type=lb worker.loadbalancerRemote.balance_workers=myAppRemoteInstance1,myAppRemoteInstance2,myAppRemoteInstance3,myAppRemoteInstance4 worker.loadbalancerRemote.sticky_session=True

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  • Node.js Adventure - Host Node.js on Windows Azure Worker Role

    - by Shaun
    In my previous post I demonstrated about how to develop and deploy a Node.js application on Windows Azure Web Site (a.k.a. WAWS). WAWS is a new feature in Windows Azure platform. Since it’s low-cost, and it provides IIS and IISNode components so that we can host our Node.js application though Git, FTP and WebMatrix without any configuration and component installation. But sometimes we need to use the Windows Azure Cloud Service (a.k.a. WACS) and host our Node.js on worker role. Below are some benefits of using worker role. - WAWS leverages IIS and IISNode to host Node.js application, which runs in x86 WOW mode. It reduces the performance comparing with x64 in some cases. - WACS worker role does not need IIS, hence there’s no restriction of IIS, such as 8000 concurrent requests limitation. - WACS provides more flexibility and controls to the developers. For example, we can RDP to the virtual machines of our worker role instances. - WACS provides the service configuration features which can be changed when the role is running. - WACS provides more scaling capability than WAWS. In WAWS we can have at most 3 reserved instances per web site while in WACS we can have up to 20 instances in a subscription. - Since when using WACS worker role we starts the node by ourselves in a process, we can control the input, output and error stream. We can also control the version of Node.js.   Run Node.js in Worker Role Node.js can be started by just having its execution file. This means in Windows Azure, we can have a worker role with the “node.exe” and the Node.js source files, then start it in Run method of the worker role entry class. Let’s create a new windows azure project in Visual Studio and add a new worker role. Since we need our worker role execute the “node.exe” with our application code we need to add the “node.exe” into our project. Right click on the worker role project and add an existing item. By default the Node.js will be installed in the “Program Files\nodejs” folder so we can navigate there and add the “node.exe”. Then we need to create the entry code of Node.js. In WAWS the entry file must be named “server.js”, which is because it’s hosted by IIS and IISNode and IISNode only accept “server.js”. But here as we control everything we can choose any files as the entry code. For example, I created a new JavaScript file named “index.js” in project root. Since we created a C# Windows Azure project we cannot create a JavaScript file from the context menu “Add new item”. We have to create a text file, and then rename it to JavaScript extension. After we added these two files we should set their “Copy to Output Directory” property to “Copy Always”, or “Copy if Newer”. Otherwise they will not be involved in the package when deployed. Let’s paste a very simple Node.js code in the “index.js” as below. As you can see I created a web server listening at port 12345. 1: var http = require("http"); 2: var port = 12345; 3:  4: http.createServer(function (req, res) { 5: res.writeHead(200, { "Content-Type": "text/plain" }); 6: res.end("Hello World\n"); 7: }).listen(port); 8:  9: console.log("Server running at port %d", port); Then we need to start “node.exe” with this file when our worker role was started. This can be done in its Run method. I found the Node.js and entry JavaScript file name, and then create a new process to run it. Our worker role will wait for the process to be exited. If everything is OK once our web server was opened the process will be there listening for incoming requests, and should not be terminated. The code in worker role would be like this. 1: public override void Run() 2: { 3: // This is a sample worker implementation. Replace with your logic. 4: Trace.WriteLine("NodejsHost entry point called", "Information"); 5:  6: // retrieve the node.exe and entry node.js source code file name. 7: var node = Environment.ExpandEnvironmentVariables(@"%RoleRoot%\approot\node.exe"); 8: var js = "index.js"; 9:  10: // prepare the process starting of node.exe 11: var info = new ProcessStartInfo(node, js) 12: { 13: CreateNoWindow = false, 14: ErrorDialog = true, 15: WindowStyle = ProcessWindowStyle.Normal, 16: UseShellExecute = false, 17: WorkingDirectory = Environment.ExpandEnvironmentVariables(@"%RoleRoot%\approot") 18: }; 19: Trace.WriteLine(string.Format("{0} {1}", node, js), "Information"); 20:  21: // start the node.exe with entry code and wait for exit 22: var process = Process.Start(info); 23: process.WaitForExit(); 24: } Then we can run it locally. In the computer emulator UI the worker role started and it executed the Node.js, then Node.js windows appeared. Open the browser to verify the website hosted by our worker role. Next let’s deploy it to azure. But we need some additional steps. First, we need to create an input endpoint. By default there’s no endpoint defined in a worker role. So we will open the role property window in Visual Studio, create a new input TCP endpoint to the port we want our website to use. In this case I will use 80. Even though we created a web server we should add a TCP endpoint of the worker role, since Node.js always listen on TCP instead of HTTP. And then changed the “index.js”, let our web server listen on 80. 1: var http = require("http"); 2: var port = 80; 3:  4: http.createServer(function (req, res) { 5: res.writeHead(200, { "Content-Type": "text/plain" }); 6: res.end("Hello World\n"); 7: }).listen(port); 8:  9: console.log("Server running at port %d", port); Then publish it to Windows Azure. And then in browser we can see our Node.js website was running on WACS worker role. We may encounter an error if we tried to run our Node.js website on 80 port at local emulator. This is because the compute emulator registered 80 and map the 80 endpoint to 81. But our Node.js cannot detect this operation. So when it tried to listen on 80 it will failed since 80 have been used.   Use NPM Modules When we are using WAWS to host Node.js, we can simply install modules we need, and then just publish or upload all files to WAWS. But if we are using WACS worker role, we have to do some extra steps to make the modules work. Assuming that we plan to use “express” in our application. Firstly of all we should download and install this module through NPM command. But after the install finished, they are just in the disk but not included in the worker role project. If we deploy the worker role right now the module will not be packaged and uploaded to azure. Hence we need to add them to the project. On solution explorer window click the “Show all files” button, select the “node_modules” folder and in the context menu select “Include In Project”. But that not enough. We also need to make all files in this module to “Copy always” or “Copy if newer”, so that they can be uploaded to azure with the “node.exe” and “index.js”. This is painful step since there might be many files in a module. So I created a small tool which can update a C# project file, make its all items as “Copy always”. The code is very simple. 1: static void Main(string[] args) 2: { 3: if (args.Length < 1) 4: { 5: Console.WriteLine("Usage: copyallalways [project file]"); 6: return; 7: } 8:  9: var proj = args[0]; 10: File.Copy(proj, string.Format("{0}.bak", proj)); 11:  12: var xml = new XmlDocument(); 13: xml.Load(proj); 14: var nsManager = new XmlNamespaceManager(xml.NameTable); 15: nsManager.AddNamespace("pf", "http://schemas.microsoft.com/developer/msbuild/2003"); 16:  17: // add the output setting to copy always 18: var contentNodes = xml.SelectNodes("//pf:Project/pf:ItemGroup/pf:Content", nsManager); 19: UpdateNodes(contentNodes, xml, nsManager); 20: var noneNodes = xml.SelectNodes("//pf:Project/pf:ItemGroup/pf:None", nsManager); 21: UpdateNodes(noneNodes, xml, nsManager); 22: xml.Save(proj); 23:  24: // remove the namespace attributes 25: var content = xml.InnerXml.Replace("<CopyToOutputDirectory xmlns=\"\">", "<CopyToOutputDirectory>"); 26: xml.LoadXml(content); 27: xml.Save(proj); 28: } 29:  30: static void UpdateNodes(XmlNodeList nodes, XmlDocument xml, XmlNamespaceManager nsManager) 31: { 32: foreach (XmlNode node in nodes) 33: { 34: var copyToOutputDirectoryNode = node.SelectSingleNode("pf:CopyToOutputDirectory", nsManager); 35: if (copyToOutputDirectoryNode == null) 36: { 37: var n = xml.CreateNode(XmlNodeType.Element, "CopyToOutputDirectory", null); 38: n.InnerText = "Always"; 39: node.AppendChild(n); 40: } 41: else 42: { 43: if (string.Compare(copyToOutputDirectoryNode.InnerText, "Always", true) != 0) 44: { 45: copyToOutputDirectoryNode.InnerText = "Always"; 46: } 47: } 48: } 49: } Please be careful when use this tool. I created only for demo so do not use it directly in a production environment. Unload the worker role project, execute this tool with the worker role project file name as the command line argument, it will set all items as “Copy always”. Then reload this worker role project. Now let’s change the “index.js” to use express. 1: var express = require("express"); 2: var app = express(); 3:  4: var port = 80; 5:  6: app.configure(function () { 7: }); 8:  9: app.get("/", function (req, res) { 10: res.send("Hello Node.js!"); 11: }); 12:  13: app.get("/User/:id", function (req, res) { 14: var id = req.params.id; 15: res.json({ 16: "id": id, 17: "name": "user " + id, 18: "company": "IGT" 19: }); 20: }); 21:  22: app.listen(port); Finally let’s publish it and have a look in browser.   Use Windows Azure SQL Database We can use Windows Azure SQL Database (a.k.a. WACD) from Node.js as well on worker role hosting. Since we can control the version of Node.js, here we can use x64 version of “node-sqlserver” now. This is better than if we host Node.js on WAWS since it only support x86. Just install the “node-sqlserver” module from NPM, copy the “sqlserver.node” from “Build\Release” folder to “Lib” folder. Include them in worker role project and run my tool to make them to “Copy always”. Finally update the “index.js” to use WASD. 1: var express = require("express"); 2: var sql = require("node-sqlserver"); 3:  4: var connectionString = "Driver={SQL Server Native Client 10.0};Server=tcp:{SERVER NAME}.database.windows.net,1433;Database={DATABASE NAME};Uid={LOGIN}@{SERVER NAME};Pwd={PASSWORD};Encrypt=yes;Connection Timeout=30;"; 5: var port = 80; 6:  7: var app = express(); 8:  9: app.configure(function () { 10: app.use(express.bodyParser()); 11: }); 12:  13: app.get("/", function (req, res) { 14: sql.open(connectionString, function (err, conn) { 15: if (err) { 16: console.log(err); 17: res.send(500, "Cannot open connection."); 18: } 19: else { 20: conn.queryRaw("SELECT * FROM [Resource]", function (err, results) { 21: if (err) { 22: console.log(err); 23: res.send(500, "Cannot retrieve records."); 24: } 25: else { 26: res.json(results); 27: } 28: }); 29: } 30: }); 31: }); 32:  33: app.get("/text/:key/:culture", function (req, res) { 34: sql.open(connectionString, function (err, conn) { 35: if (err) { 36: console.log(err); 37: res.send(500, "Cannot open connection."); 38: } 39: else { 40: var key = req.params.key; 41: var culture = req.params.culture; 42: var command = "SELECT * FROM [Resource] WHERE [Key] = '" + key + "' AND [Culture] = '" + culture + "'"; 43: conn.queryRaw(command, function (err, results) { 44: if (err) { 45: console.log(err); 46: res.send(500, "Cannot retrieve records."); 47: } 48: else { 49: res.json(results); 50: } 51: }); 52: } 53: }); 54: }); 55:  56: app.get("/sproc/:key/:culture", function (req, res) { 57: sql.open(connectionString, function (err, conn) { 58: if (err) { 59: console.log(err); 60: res.send(500, "Cannot open connection."); 61: } 62: else { 63: var key = req.params.key; 64: var culture = req.params.culture; 65: var command = "EXEC GetItem '" + key + "', '" + culture + "'"; 66: conn.queryRaw(command, function (err, results) { 67: if (err) { 68: console.log(err); 69: res.send(500, "Cannot retrieve records."); 70: } 71: else { 72: res.json(results); 73: } 74: }); 75: } 76: }); 77: }); 78:  79: app.post("/new", function (req, res) { 80: var key = req.body.key; 81: var culture = req.body.culture; 82: var val = req.body.val; 83:  84: sql.open(connectionString, function (err, conn) { 85: if (err) { 86: console.log(err); 87: res.send(500, "Cannot open connection."); 88: } 89: else { 90: var command = "INSERT INTO [Resource] VALUES ('" + key + "', '" + culture + "', N'" + val + "')"; 91: conn.queryRaw(command, function (err, results) { 92: if (err) { 93: console.log(err); 94: res.send(500, "Cannot retrieve records."); 95: } 96: else { 97: res.send(200, "Inserted Successful"); 98: } 99: }); 100: } 101: }); 102: }); 103:  104: app.listen(port); Publish to azure and now we can see our Node.js is working with WASD through x64 version “node-sqlserver”.   Summary In this post I demonstrated how to host our Node.js in Windows Azure Cloud Service worker role. By using worker role we can control the version of Node.js, as well as the entry code. And it’s possible to do some pre jobs before the Node.js application started. It also removed the IIS and IISNode limitation. I personally recommended to use worker role as our Node.js hosting. But there are some problem if you use the approach I mentioned here. The first one is, we need to set all JavaScript files and module files as “Copy always” or “Copy if newer” manually. The second one is, in this way we cannot retrieve the cloud service configuration information. For example, we defined the endpoint in worker role property but we also specified the listening port in Node.js hardcoded. It should be changed that our Node.js can retrieve the endpoint. But I can tell you it won’t be working here. In the next post I will describe another way to execute the “node.exe” and Node.js application, so that we can get the cloud service configuration in Node.js. I will also demonstrate how to use Windows Azure Storage from Node.js by using the Windows Azure Node.js SDK.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • SQL SERVER – Find Max Worker Count using DMV – 32 Bit and 64 Bit

    - by pinaldave
    During several recent training courses, I found it very interesting that Worker Thread is not quite known to everyone despite the fact that it is a very important feature. At some point in the discussion, one of the attendees mentioned that we can double the Worker Thread if we double the CPU (add the same number of CPU that we have on current system). The same discussion has triggered this quick article. Here is the DMV which can be used to find out Max Worker Count SELECT max_workers_count FROM sys.dm_os_sys_info Let us run the above query on my system and find the results. As my system is 32 bit and I have two CPU, the Max Worker Count is displayed as 512. To address the previous discussion, adding more CPU does not necessarily double the Worker Count. In fact, the logic behind this simple principle is as follows: For x86 (32-bit) upto 4 logical processors  max worker threads = 256 For x86 (32-bit) more than 4 logical processors  max worker threads = 256 + ((# Procs – 4) * 8) For x64 (64-bit) upto 4 logical processors  max worker threads = 512 For x64 (64-bit) more than 4 logical processors  max worker threads = 512+ ((# Procs – 4) * 8) In addition to this, you can configure the Max Worker Thread by using SSMS. Go to Server Node >> Right Click and Select Property >> Select Process and modify setting under Worker Threads. According to Book On Line, the default Worker Thread settings are appropriate for most of the systems. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL System Table, SQL Tips and Tricks, T SQL, Technology Tagged: SQL DMV

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  • Monitor.Wait, Pulse - When worker thread should conditionally behave as an actual worker thread

    - by Griever
    My particular scenario: - Main thread starts a worker thread. - Main thread needs to block itself until either worker thread is completed (yeah funny) or worker thread itself informs main thread to go on Alright, so what I did in main thread: wokerThread.Start(lockObj); lock(lockObj) Monitor.Wait(lockObj); Somewhere in worker thread: if(mainThreadShouldGoOn) lock(lockObj) Monitor.Pulse(lockObj); Also, at the end of worker thread: lock(lockObj) Monitor.Pulse(lockObj); So far, it's working perfect. But is it a good solution? Is there a better one?

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  • SQL SERVER – Extending SQL Azure with Azure worker role – Guest Post by Paras Doshi

    - by pinaldave
    This is guest post by Paras Doshi. Paras Doshi is a research Intern at SolidQ.com and a Microsoft student partner. He is currently working in the domain of SQL Azure. SQL Azure is nothing but a SQL server in the cloud. SQL Azure provides benefits such as on demand rapid provisioning, cost-effective scalability, high availability and reduced management overhead. To see an introduction on SQL Azure, check out the post by Pinal here In this article, we are going to discuss how to extend SQL Azure with the Azure worker role. In other words, we will attempt to write a custom code and host it in the Azure worker role; the aim is to add some features that are not available with SQL Azure currently or features that need to be customized for flexibility. This way we extend the SQL Azure capability by building some solutions that run on Azure as worker roles. To understand Azure worker role, think of it as a windows service in cloud. Azure worker role can perform background processes, and to handle processes such as synchronization and backup, it becomes our ideal tool. First, we will focus on writing a worker role code that synchronizes SQL Azure databases. Before we do so, let’s see some scenarios in which synchronization between SQL Azure databases is beneficial: scaling out access over multiple databases enables us to handle workload efficiently As of now, SQL Azure database can be hosted in one of any six datacenters. By synchronizing databases located in different data centers, one can extend the data by enabling access to geographically distributed data Let us see some scenarios in which SQL server to SQL Azure database synchronization is beneficial To backup SQL Azure database on local infrastructure Rather than investing in local infrastructure for increased workloads, such workloads could be handled by cloud Ability to extend data to different datacenters located across the world to enable efficient data access from remote locations Now, let us develop cloud-based app that synchronizes SQL Azure databases. For an Introduction to developing cloud based apps, click here Now, in this article, I aim to provide a bird’s eye view of how a code that synchronizes SQL Azure databases look like and then list resources that can help you develop the solution from scratch. Now, if you newly add a worker role to the cloud-based project, this is how the code will look like. (Note: I have added comments to the skeleton code to point out the modifications that will be required in the code to carry out the SQL Azure synchronization. Note the placement of Setup() and Sync() function.) Click here (http://parasdoshi1989.files.wordpress.com/2011/06/code-snippet-1-for-extending-sql-azure-with-azure-worker-role1.pdf ) Enabling SQL Azure databases synchronization through sync framework is a two-step process. In the first step, the database is provisioned and sync framework creates tracking tables, stored procedures, triggers, and tables to store metadata to enable synchronization. This is one time step. The code for the same is put in the setup() function which is called once when the worker role starts. Now, the second step is continuous (or on demand) synchronization of SQL Azure databases by propagating changes between databases. This is done on a continuous basis by calling the sync() function in the while loop. The code logic to synchronize changes between SQL Azure databases should be put in the sync() function. Discussing the coding part step by step is out of the scope of this article. Therefore, let me suggest you a resource, which is given here. Also, note that before you start developing the code, you will need to install SYNC framework 2.1 SDK (download here). Further, you will reference some libraries before you start coding. Details regarding the same are available in the article that I just pointed to. You will be charged for data transfers if the databases are not in the same datacenter. For pricing information, go here Currently, a tool named DATA SYNC, which is built on top of sync framework, is available in CTP that allows SQL Azure <-> SQL server and SQL Azure <-> SQL Azure synchronization (without writing single line of code); however, in some cases, the custom code shown in this blogpost provides flexibility that is not available with Data SYNC. For instance, filtering is not supported in the SQL Azure DATA SYNC CTP2; if you wish to have such a functionality now, then you have the option of developing a custom code using SYNC Framework. Now, this code can be easily extended to synchronize at some schedule. Let us say we want the databases to get synchronized every day at 10:00 pm. This is what the code will look like now: (http://parasdoshi1989.files.wordpress.com/2011/06/code-snippet-2-for-extending-sql-azure-with-azure-worker-role.pdf) Don’t you think that by writing such a code, we are imitating the functionality provided by the SQL server agent for a SQL server? Think about it. We are scheduling our administrative task by writing custom code – in other words, we have developed a “Light weight SQL server agent for SQL Azure!” Since the SQL server agent is not currently available in cloud, we have developed a solution that enables us to schedule tasks, and thus we have extended SQL Azure with the Azure worker role! Now if you wish to track jobs, you can do so by storing this data in SQL Azure (or Azure tables). The reason is that Windows Azure is a stateless platform, and we will need to store the state of the job ourselves and the choice that you have is SQL Azure or Azure tables. Note that this solution requires custom code and also it is not UI driven; however, for now, it can act as a temporary solution until SQL server agent is made available in the cloud. Moreover, this solution does not encompass functionalities that a SQL server agent provides, but it does open up an interesting avenue to schedule some of the tasks such as backup and synchronization of SQL Azure databases by writing some custom code in the Azure worker role. Now, let us see one more possibility – i.e., running BCP through a worker role in Azure-hosted services and then uploading the backup files either locally or on blobs. If you upload it locally, then consider the data transfer cost. If you upload it to blobs residing in the same datacenter, then no transfer cost applies but the cost on blob size applies. So, before choosing the option, you need to evaluate your preferences keeping the cost associated with each option in mind. In this article, I have shown that Azure worker role solution could be developed to synchronize SQL Azure databases. Moreover, a light-weight SQL server agent for SQL Azure can be developed. Also we discussed the possibility of running BCP through a worker role in Azure-hosted services for backing up our precious SQL Azure data. Thus, we can extend SQL Azure with the Azure worker role. But remember: you will be charged for running Azure worker roles. So at the end of the day, you need to ask – am I willing to build a custom code and pay money to achieve this functionality? I hope you found this blog post interesting. If you have any questions/feedback, you can comment below or you can mail me at Paras[at]student-partners[dot]com Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Azure, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Viability of Apache (MPM Worker), FastCGI PHP 4/5.2/5.3, and MySQL 5

    - by Adrian
    My server will be hosting numerous PHP web applications ranging from Joomla, Drupal, and some legacy (read: PHP4) and other custom-built code inherited from clients. This will be a development machine used by a dozen or so web developers and issues like fluctuating loads or particularly high load expectations are not important. Now, my question: are there any concerns I should know about when using Apache w/ MPM Worker, PHP 4/PHP 5.2/PHP 5.3 (all via FastCGI), and MySQL 5 (with a query cache of 64MB)? I have not tested the various applications extensively and I have only recently learned how to install PHP and utilize it via FastCGI (rather than mod_php, which in this case seemed impossible (considering the multiple versions of PHP and the desire to use MPM Worker over MPM Prefork)). I have come to understand that there could be concerns regarding XCache and APC, namely non-thread-safety issues where data becomes corrupted and the capability to use MPM Worker becomes null and void. Is this a valid concern? I have been using my personal testing server (running Ubuntu Server Edition 10.04 in VirtualBox) which has 2GB of RAM available to it. Here is the configuration used (the actual server will likely use a configuration more tailored to suit it's purposes): Apache: Server version: Apache/2.2.14 (Ubuntu) Server built: Apr 13 2010 20:22:19 Server's Module Magic Number: 20051115:23 Server loaded: APR 1.3.8, APR-Util 1.3.9 Compiled using: APR 1.3.8, APR-Util 1.3.9 Architecture: 64-bit Server MPM: Worker threaded: yes (fixed thread count) forked: yes (variable process count) Worker: <IfModule mpm_worker_module> StartServers 2 MinSpareThreads 25 MaxSpareThreads 75 ThreadLimit 64 ThreadsPerChild 25 MaxClients 400 MaxRequestsPerChild 2000 </IfModule> PHP ./configure (PHP 4.4.9, PHP 5.2.13, PHP 5.3.2): --enable-bcmath \ --enable-calendar \ --enable-exif \ --enable-ftp \ --enable-mbstring \ --enable-pcntl \ --enable-soap \ --enable-sockets \ --enable-sqlite-utf8 \ --enable-wddx \ --enable-zip \ --enable-fastcgi \ --with-zlib \ --with-gettext \ Apache php-fastcgi-setup.conf FastCgiServer /var/www/cgi-bin/php-cgi-5.3.2 FastCgiServer /var/www/cgi-bin/php-cgi-5.2.13 FastCgiServer /var/www/cgi-bin/php-cgi-4.4.9 ScriptAlias /cgi-bin-php/ /var/www/cgi-bin/

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  • Prefork or Worker MPM for amazon xlarge server?

    - by Netismine
    I'm trying to measure would it be better to have prefork or worker mpm apache module for the server I'm working on, which is Amazon X-Large 15 GB memory 8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each) and that will run a Magento website with about 50 active users at once. Site serves a lot of images and about 45 requests per page. Images sometimes hang, so it seems worker would be a better option? Thanks

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  • Reducing memory for worker MPM in Apache

    - by ShyM
    I've moved from the prefork MPM to the worker MPM due to a process limit I was hitting on my VPS. However, memory usage increased after switching over (which is odd since the worker MPM is supposed to have a smaller memory footprint?). Most of them belong to php-cgi processes. Is there something I'm doing wrong? I have around 20 sites on it, each with a different fcgi wrapper script. Could that be a reason?

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  • Windows Azure worker roles: One big job or many small jobs?

    - by Ryan Elkins
    Is there any inherent advantage when using multiple workers to process pieces of procedural code versus processing the entire load? In other words, if my workflow looks like this: Get work from queue0 and do A Store result from A in queue1 Get result from queue 1 and do B Store result from B in queue2 Get result from queue2 and do C Is there an inherent advantage to using 3 workers who each do the entire process themselves versus 3 workers that each do a part of the work (Worker 1 does 1 & 2, worker 2 does 3 & 4, worker 3 does 5). If we only care about working being done (finished with step 5) it would seem that it scales the same way (once you're using at least 3 workers). Maybe the big job is better because workers with that setup have less bottleneck issues?

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  • Switching from prefork MPM to worker MPM + php-fpm on ubuntu

    - by Shane
    All tutorials I found were how to fresh install worker MPM + PHP-FPM, since my wordpress blog's already up and running with prefork MPM, correct me if I'm wrong in the simulated installation process: I'm on ubuntu and according to some tutorials, the following lines would do all the tricks: apt-get install apache2-mpm-worker libapache2-mod-fastcgi php5-fpm php5-gd a2enmod actions fastcgi alias Then you setup configuration in /etc/apache2/conf.d/php5-fpm.conf: <IfModule mod_fastcgi.c> AddHandler php5-fcgi .php Action php5-fcgi /php5-fcgi Alias /php5-fcgi /usr/lib/cgi-bin/php5-fcgi FastCgiExternalServer /usr/lib/cgi-bin/php5-fcgi -host 127.0.0.1:9000 -pass-header Authorization </IfModule> After all these, restart: service apache2 restart && service php5-fpm restart Question: 1) Would it cause any down time in the whole process for previously running sites with prefork MPM? 2) Do you have to change any already existent configuration files like php or mysql or apache2(would they take effect immediately after the switch without you doing anything)? 3) I've already have apc up and running, do you have to re-install/re-configure it after the switch? 4) How do you find out if apache2 is working in worker MPM mode as expected? Thanks a lot!

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  • A Method for Reducing Contention and Overhead in Worker Queues for Multithreaded Java Applications

    - by Janice J. Heiss
    A java.net article, rich in practical resources, by IBM India Labs’ Sathiskumar Palaniappan, Kavitha Varadarajan, and Jayashree Viswanathan, explores the challenge of writing code in a way that that effectively makes use of the resources of modern multicore processors and multiprocessor servers.As the article states: “Many server applications, such as Web servers, application servers, database servers, file servers, and mail servers, maintain worker queues and thread pools to handle large numbers of short tasks that arrive from remote sources. In general, a ‘worker queue’ holds all the short tasks that need to be executed, and the threads in the thread pool retrieve the tasks from the worker queue and complete the tasks. Since multiple threads act on the worker queue, adding tasks to and deleting tasks from the worker queue needs to be synchronized, which introduces contention in the worker queue.” The article goes on to explain ways that developers can reduce contention by maintaining one queue per thread. It also demonstrates a work-stealing technique that helps in effectively utilizing the CPU in multicore systems. Read the rest of the article here.

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  • automatic IIS worker process recycle fails

    - by Sander Rijken
    The server is set to its default configuration to recycle the app pool every 1740 minutes. When this happens the following message is logged: A worker process with process id of '1234' serving application pool 'XX' has requested a recycle because the worker process reached its allowed processing time limit. Directly after logging this message, the web site is unresponsive. The only way to get it back online is by running iisreset manually. Does anyone know a fix for this behavior, other than turning the recycle feature off? Is it a known problem?

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  • Apache worker is crashing after 3.000 users

    - by user1618606
    I activated Apache Worker on my VPS and I'm having problems, 'cause the website is crashing when 3000 users are accessing the website. I'm using http://whos.amung.us/stats/2jzwlvbhvpft/ as counter. My Apache Worker configuration: KeepAlive On MaxKeepAliveRequests 0 KeepAliveTimeout 1 <IfModule mpm_worker_module> ServerLimit 20000 StartServer 8000 MinSpareThreads 10400 MaxSpareThreads 14200 ThreadLimit 5 ThreadsPerChild 5 MaxClients 20000 MaxRequestsPerChild 0 </IfModule> The VPS have the SO: Debian 64 LAMP, memory: 14gb and CPU: 24ghz What I could to do to give a best performance?

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  • Apache 2.2, worker mpm, mod_fcgid and PHP: Can't apply process slot

    - by mopoke
    We're having an issue on an apache server where every 15 to 20 minutes it stops serving PHP requests entirely. On occasions it will return a 503 error, other times it will recover enough to serve the page but only after a delay of a minute or more. Static content is still served during that time. In the log file, there's errors reported along the lines of: [Wed Sep 28 10:45:39 2011] [warn] mod_fcgid: can't apply process slot for /xxx/ajaxfolder/ajax_features.php [Wed Sep 28 10:45:41 2011] [warn] mod_fcgid: can't apply process slot for /xxx/statics/poll/index.php [Wed Sep 28 10:45:45 2011] [warn] mod_fcgid: can't apply process slot for /xxx/index.php [Wed Sep 28 10:45:45 2011] [warn] mod_fcgid: can't apply process slot for /xxx/index.php There is RAM free and, indeed, it seems that more php processes get spawned. /server-status shows lots of threads in the "W" state as well as some FastCGI processes in "Exiting(communication error)" state. I rebuilt mod_fcgid from source as the packaged version was quite old. It's using current stable version (2.3.6) of mod_fcgid. FCGI config: FcgidBusyScanInterval 30 FcgidBusyTimeout 60 FcgidIdleScanInterval 30 FcgidIdleTimeout 45 FcgidIOTimeout 60 FcgidConnectTimeout 20 FcgidMaxProcesses 100 FcgidMaxRequestsPerProcess 500 FcgidOutputBufferSize 1048576 System info: Linux xxx.com 2.6.28-11-server #42-Ubuntu SMP Fri Apr 17 02:45:36 UTC 2009 x86_64 GNU/Linux DISTRIB_ID=Ubuntu DISTRIB_RELEASE=9.04 DISTRIB_CODENAME=jaunty DISTRIB_DESCRIPTION="Ubuntu 9.04" Apache info: Server version: Apache/2.2.11 (Ubuntu) Server built: Aug 16 2010 17:45:55 Server's Module Magic Number: 20051115:21 Server loaded: APR 1.2.12, APR-Util 1.2.12 Compiled using: APR 1.2.12, APR-Util 1.2.12 Architecture: 64-bit Server MPM: Worker threaded: yes (fixed thread count) forked: yes (variable process count) Server compiled with.... -D APACHE_MPM_DIR="server/mpm/worker" -D APR_HAS_SENDFILE -D APR_HAS_MMAP -D APR_HAVE_IPV6 (IPv4-mapped addresses enabled) -D APR_USE_SYSVSEM_SERIALIZE -D APR_USE_PTHREAD_SERIALIZE -D SINGLE_LISTEN_UNSERIALIZED_ACCEPT -D APR_HAS_OTHER_CHILD -D AP_HAVE_RELIABLE_PIPED_LOGS -D DYNAMIC_MODULE_LIMIT=128 -D HTTPD_ROOT="" -D SUEXEC_BIN="/usr/lib/apache2/suexec" -D DEFAULT_PIDLOG="/var/run/apache2.pid" -D DEFAULT_SCOREBOARD="logs/apache_runtime_status" -D DEFAULT_ERRORLOG="logs/error_log" -D AP_TYPES_CONFIG_FILE="/etc/apache2/mime.types" -D SERVER_CONFIG_FILE="/etc/apache2/apache2.conf" Apache modules loaded: alias.load auth_basic.load authn_file.load authz_default.load authz_groupfile.load authz_host.load authz_user.load autoindex.load cgi.load deflate.load dir.load env.load expires.load fcgid.load headers.load include.load mime.load negotiation.load rewrite.load setenvif.load ssl.load status.load suexec.load PHP info: PHP 5.2.6-3ubuntu4.6 with Suhosin-Patch 0.9.6.2 (cli) (built: Sep 16 2010 19:51:25) Copyright (c) 1997-2008 The PHP Group Zend Engine v2.2.0, Copyright (c) 1998-2008 Zend Technologies

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  • Apache2 Worker Starting Tons of Processes

    - by karmic
    I am installed apache2-mpm-worker and left all config files default (I've never touched them much). Is it normal that when I restart apache there is at least 20 apache processes starting? Shouldn't it be just 2 like it says in the configuration? Also, my memory seems to grow very quickly until my machine crashes. I don't have any mods installed.

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  • Weblogic / EjbGen: worker manager configuration.

    - by Guillaume
    I want to declare a worker manager to perform some work in managed thread. Weblogic documentation tells that we can declare a global worker manager using the admin console or declare it in an ejb-jar.xml config file. I want to use the second option. But my ejb-jar.xml is generated by the ejbgen tool. There is no tag in ejbgen that would allow me to declare a worker manager. So how should I create a local worker manager declaration ?

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  • Advice: Python Framework Server/Worker Queue management (not Website)

    - by Muppet Geoff
    I am looking for some advice/opinions of which Python Framework to use in an implementation of multiple 'Worker' PCs co-ordinated from a central Queue Manager. For completeness, the 'Worker' PCs will be running Audio Conversion routines (which I do not need advice on, and have standalone code that works). The Audio conversion takes a long time, and I need to co-ordinate an arbitrary number of the 'Workers' from a central location, handing them conversion tasks (such as where to get the source files, or where to ask for the job configuration) with them reporting back some additional info, such as the runtime of the converted audio etc. At present, I have a script that makes a webservice call to get the 'configuration' for a conversion task, based on source files located on the worker already (we manually copy the source files to the worker, and that triggers a conversion routine). I want to change this, so that we can distribute conversion tasks ("Oy you, process this: xxx") based on availability, and in an ideal world, based on pending tasks too. There is a chance that Workers can go offline mid-conversion (but this is not likely). All the workers are Windows based, the co-ordinator can be WIndows or Linux. I have (in my initial searches) come across the following - and I know that some are cross-dependent: Celery (with RabbitMQ) Twisted Django Using a framework, rather than home-brewing, seems to make more sense to me right now. I have a limited timeframe in which to develop this functional extension. An additional consideration would be using a Framework that is compatible with PyQT/PySide so that I can write a simple UI to display Queue status etc. I appreciate that the specifics above are a little vague, and I hope that someone can offer me a pointer or two. Again: I am looking for general advice on which Python framework to investigate further, for developing a Server/Worker 'Queue management' solution, for non-web activities (this is why DJango didn't seem the right fit).

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  • Apache2 worker mpm too many processes

    - by delerious010
    I've got Apache installed with the worker mpm which seems to have too many processes active in spite of the configurations in place. I'll detail the configs below : StartServers 2 MinSpareThreads 10 MaxSpareThreads 25 ThreadsPerChild 25 MaxClients 150 Based on these settings, we should be seeing a maximum of 1 Apache control process (uid:root) and 6 Apache client processes (uid:www). This being due to MaxClients/ThreadsPerChild. However, I'm seeing a total of 1 Apache control process and 9 Apache client processes. init -- apache2(root) -- -- apache2(www) -- -- apache2(www) -- 1 thread -- -- apache2(www) -- 26 threads -- -- apache2(www) -- 26 threads init -- apache2(www) -- 2 threads -- apache2(www) -- apache2(www) -- apache2(www) We do not make it a habit of restarting Apache nor the Server, and will perform a reload 2-3 times a day at times so as to add new VHOSTs. Would anyone be able to enlighten me as to what might be causing this ? enter code here

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  • Mindtouch with fcgid - Fast CGI apache worker thread.

    - by Stephan Kristyn
    Anyone got Dekiwiki / Mindtouch running with fcgid-module? I get 504 and 500 all the time. mod_fcgid: can't apply process slot for /var/www/html/dekiwiki/index.php [Tue Dec 28 06:14:03 2010] [warn] (104)Connection reset by peer: mod_fcgid: read data from fastcgi server error. [Tue Dec 28 06:14:03 2010] [error] [client 92.75.107.53] Premature end of script headers: index.php I'm currently fiddling with SuExec and fast-cgi wrapper directory permissions, because I also employ a chrooted SFTP jail. Sometimes the first line about the process slot does not appear now. I found a solution in german and will work it through now. http://debianforum.de/forum/viewtopic.php?f=8&t=122758&start=15

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  • How to terminate a particular Azure worker role instance

    - by Oliver Bock
    Background I am trying to work out the best structure for an Azure application. Each of my worker roles will spin up multiple long-running jobs. Over time I can transfer jobs from one instance to another by switching them to a readonly mode on the source instance, spinning them up on the target instance, and then spinning the original down on the source instance. If I have too many jobs then I can tell Azure to spin up extra role instance, and use them for new jobs. Conversely if my load drops (e.g. during the night) then I can consolidate outstanding jobs to a few machines and tell Azure to give me fewer instances. The trouble is that (as I understand it) Azure provides no mechanism to allow me to decide which instance to stop. Thus I cannot know which servers to consolidate onto, and some of my jobs will die when their instance stops, causing delays for users while I restart those jobs on surviving instances. Idea 1: I decide which instance to stop, and return from its Run(). I then tell Azure to reduce my instance count by one, and hope it concludes that the broken instance is a good candidate. Has anyone tried anything like this? Idea 2: I predefine a whole bunch of different worker roles, with identical contents. I can individually stop and start them by switching their instance count from zero to one, and back again. I think this idea would work, but I don't like it because it seems to go against the natural Azure way of doing things, and because it involves me in a lot of extra bookkeeping to manage the extra worker roles. Idea 3: Live with it. Any better ideas?

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